{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Characterization of ALPACA's modules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparation\n",
    "First we read the membership file from ALPACA, the two networks, and we prepare the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
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         "2",
         "ALX4_A",
         "1",
         "0.0052476092592501"
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         "3",
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         "2",
         "0.0014446716062823"
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         "4",
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node</th>\n",
       "      <th>module</th>\n",
       "      <th>modularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALX1_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALX3_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.011</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALX4_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0014</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARGFX_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      node  module  modularity\n",
       "0   ALX1_A       1      0.0061\n",
       "1   ALX3_A       2       0.011\n",
       "2   ALX4_A       1      0.0052\n",
       "3     AR_A       2      0.0014\n",
       "4  ARGFX_A       2       0.012"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output directory\n",
    "import os\n",
    "outputDir = \"../results/alpaca-batch-coad-subtype-connnectivity-20250317/\"\n",
    "if not os.path.exists(outputDir):\n",
    "    os.makedirs(outputDir)\n",
    "# ALPACA membership file\n",
    "membership_fn = \"../data/processed/alpaca-batch-coad-subtype-20240510/membership_csm2_csm4.csv\"\n",
    "# Read membership\n",
    "membership = pd.read_csv(membership_fn)\n",
    "membership.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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         "ALX4",
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         "3",
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         "2",
         "0.0014446716062823",
         "AR",
         "A"
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         "4",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node</th>\n",
       "      <th>module</th>\n",
       "      <th>modularity</th>\n",
       "      <th>node_name</th>\n",
       "      <th>node_type</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALX1_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.006097</td>\n",
       "      <td>ALX1</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALX3_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.011064</td>\n",
       "      <td>ALX3</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALX4_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005248</td>\n",
       "      <td>ALX4</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.001445</td>\n",
       "      <td>AR</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARGFX_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.012254</td>\n",
       "      <td>ARGFX</td>\n",
       "      <td>A</td>\n",
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      ],
      "text/plain": [
       "      node  module  modularity node_name node_type\n",
       "0   ALX1_A       1    0.006097      ALX1         A\n",
       "1   ALX3_A       2    0.011064      ALX3         A\n",
       "2   ALX4_A       1    0.005248      ALX4         A\n",
       "3     AR_A       2    0.001445        AR         A\n",
       "4  ARGFX_A       2    0.012254     ARGFX         A"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clean membership table splitting node into node_name and node_type\n",
    "membership['node_name'] = membership.node.str.split('_').str[0]\n",
    "membership['node_type'] = membership.node.str.split('_').str[1]\n",
    "membership.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of PANDA connectivity\n",
    "\n",
    "Here, using the modules identified by ALPACA, we check the differences in connectivity between the two subtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the two networks\n",
    "cms2_net = pd.read_csv(\"../data/processed/batch-coad-subtype-20240510/tcga_coad_cms2/analysis/panda/panda_tcga_coad_cms2.txt\", sep=\" \", index_col=0)\n",
    "cms4_net = pd.read_csv(\"../data/processed/batch-coad-subtype-20240510/tcga_coad_cms4/analysis/panda/panda_tcga_coad_cms4.txt\", sep=\" \", index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare an edge membership table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# melt the dataframe and keep the index\n",
    "cms2_long = cms2_net.melt(ignore_index = False, var_name = 'gene', value_name='cms2').reset_index()\n",
    "cms4_long = cms4_net.melt(ignore_index = False, var_name = 'gene', value_name='cms4').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19590053, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the two nets\n",
    "nets = cms2_long.merge(cms4_long, on =['tf','gene'], how = 'inner')\n",
    "nets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>ALX4</td>\n",
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       "      <td>0.552226</td>\n",
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       "      <th>3</th>\n",
       "      <td>AR</td>\n",
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       "      <td>-0.862890</td>\n",
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       "      <th>4</th>\n",
       "      <td>ARGFX</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>0.887296</td>\n",
       "      <td>0.290060</td>\n",
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       "      tf             gene      cms2      cms4\n",
       "0   ALX1  ENSG00000000003  1.639950  0.701865\n",
       "1   ALX3  ENSG00000000003  0.463252  0.087116\n",
       "2   ALX4  ENSG00000000003  1.347992  0.552226\n",
       "3     AR  ENSG00000000003 -0.986745 -0.862890\n",
       "4  ARGFX  ENSG00000000003  0.887296  0.290060"
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   "source": [
    "nets.head()"
   ]
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   "execution_count": 12,
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       "      <th>tf</th>\n",
       "      <th>gene</th>\n",
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       "      <th>node_type_tf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALX1</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>1.639950</td>\n",
       "      <td>0.701865</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>ALX1_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.006097</td>\n",
       "      <td>ALX1</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALX3</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>0.463252</td>\n",
       "      <td>0.087116</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>ALX3_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.011064</td>\n",
       "      <td>ALX3</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALX4</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>1.347992</td>\n",
       "      <td>0.552226</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>ALX4_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005248</td>\n",
       "      <td>ALX4</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>-0.986745</td>\n",
       "      <td>-0.862890</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>AR_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.001445</td>\n",
       "      <td>AR</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARGFX</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>0.887296</td>\n",
       "      <td>0.290060</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>ARGFX_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.012254</td>\n",
       "      <td>ARGFX</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tf             gene      cms2      cms4          node_gene  module_gene  \\\n",
       "0   ALX1  ENSG00000000003  1.639950  0.701865  ENSG00000000003_B            7   \n",
       "1   ALX3  ENSG00000000003  0.463252  0.087116  ENSG00000000003_B            7   \n",
       "2   ALX4  ENSG00000000003  1.347992  0.552226  ENSG00000000003_B            7   \n",
       "3     AR  ENSG00000000003 -0.986745 -0.862890  ENSG00000000003_B            7   \n",
       "4  ARGFX  ENSG00000000003  0.887296  0.290060  ENSG00000000003_B            7   \n",
       "\n",
       "   modularity_gene   node_name_gene node_type_gene  node_tf  module_tf  \\\n",
       "0         0.002225  ENSG00000000003              B   ALX1_A          1   \n",
       "1         0.002225  ENSG00000000003              B   ALX3_A          2   \n",
       "2         0.002225  ENSG00000000003              B   ALX4_A          1   \n",
       "3         0.002225  ENSG00000000003              B     AR_A          2   \n",
       "4         0.002225  ENSG00000000003              B  ARGFX_A          2   \n",
       "\n",
       "   modularity_tf node_name_tf node_type_tf  \n",
       "0       0.006097         ALX1            A  \n",
       "1       0.011064         ALX3            A  \n",
       "2       0.005248         ALX4            A  \n",
       "3       0.001445           AR            A  \n",
       "4       0.012254        ARGFX            A  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In this table we report each edge (tf-gene) , the CMS2 and CMS4 values and the membership of the gene and the tf\n",
    "edge_membership = nets.merge(membership, left_on='gene', right_on='node_name', how = 'left')\n",
    "edge_membership = edge_membership.merge(membership, left_on='tf', right_on='node_name', how = 'left', suffixes=('_gene', '_tf'))\n",
    "edge_membership.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
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        {
         "name": "index",
         "rawType": "int64",
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         "name": "tf",
         "rawType": "object",
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        },
        {
         "name": "gene",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "cms2",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "cms4",
         "rawType": "float64",
         "type": "float"
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         "name": "node_gene",
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         "type": "string"
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         "name": "module_gene",
         "rawType": "int64",
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         "name": "node_name_gene",
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        [
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         "ENSG00000000003",
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         "2",
         "0.0013605016794684",
         "BATF",
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         "False",
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        ],
        [
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         "BATF3",
         "ENSG00000000003",
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         "ENSG00000000003_B",
         "7",
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         "ENSG00000000003",
         "B",
         "BATF3_A",
         "5",
         "0.032231334119926",
         "BATF3",
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         "False",
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        ],
        [
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         "BBX",
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         "-0.2571074293813081",
         "ENSG00000000003_B",
         "7",
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         "ENSG00000000003",
         "B",
         "BBX_A",
         "2",
         "0.000957432562253908",
         "BBX",
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         "False",
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        ],
        [
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         "-1.01003834428802",
         "ENSG00000000003_B",
         "7",
         "0.0022248639068903",
         "ENSG00000000003",
         "B",
         "BCL11A_A",
         "4",
         "0.0018767965567204",
         "BCL11A",
         "A",
         "False",
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        ],
        [
         "30",
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         "7",
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         "ENSG00000000003",
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         "BCL11B_A",
         "4",
         "0.0020981041864811",
         "BCL11B",
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        ],
        [
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         "B",
         "BCL6_A",
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         "0.0073183465431914",
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         "False",
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        ],
        [
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         "BCL6B",
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        ],
        [
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        [
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        "rows": 19590053
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      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tf</th>\n",
       "      <th>gene</th>\n",
       "      <th>cms2</th>\n",
       "      <th>cms4</th>\n",
       "      <th>node_gene</th>\n",
       "      <th>module_gene</th>\n",
       "      <th>modularity_gene</th>\n",
       "      <th>node_name_gene</th>\n",
       "      <th>node_type_gene</th>\n",
       "      <th>node_tf</th>\n",
       "      <th>module_tf</th>\n",
       "      <th>modularity_tf</th>\n",
       "      <th>node_name_tf</th>\n",
       "      <th>node_type_tf</th>\n",
       "      <th>same_module</th>\n",
       "      <th>edge_module</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALX1</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>1.639950</td>\n",
       "      <td>0.701865</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>ALX1_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.006097</td>\n",
       "      <td>ALX1</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALX3</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>0.463252</td>\n",
       "      <td>0.087116</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>ALX3_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.011064</td>\n",
       "      <td>ALX3</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALX4</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>1.347992</td>\n",
       "      <td>0.552226</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>ALX4_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005248</td>\n",
       "      <td>ALX4</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>-0.986745</td>\n",
       "      <td>-0.862890</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>AR_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.001445</td>\n",
       "      <td>AR</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARGFX</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>0.887296</td>\n",
       "      <td>0.290060</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>ARGFX_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.012254</td>\n",
       "      <td>ARGFX</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19590048</th>\n",
       "      <td>ZSCAN4</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>-0.292792</td>\n",
       "      <td>-0.106729</td>\n",
       "      <td>ENSG00000284594_B</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>B</td>\n",
       "      <td>ZSCAN4_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.002642</td>\n",
       "      <td>ZSCAN4</td>\n",
       "      <td>A</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19590049</th>\n",
       "      <td>ZSCAN5</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>0.836854</td>\n",
       "      <td>0.792892</td>\n",
       "      <td>ENSG00000284594_B</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>B</td>\n",
       "      <td>ZSCAN5_A</td>\n",
       "      <td>4</td>\n",
       "      <td>0.002277</td>\n",
       "      <td>ZSCAN5</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19590050</th>\n",
       "      <td>ZSCAN5C</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>-0.752496</td>\n",
       "      <td>-0.782719</td>\n",
       "      <td>ENSG00000284594_B</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>B</td>\n",
       "      <td>ZSCAN5C_A</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>ZSCAN5C</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19590051</th>\n",
       "      <td>ZSCAN9</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>-1.703456</td>\n",
       "      <td>-1.760072</td>\n",
       "      <td>ENSG00000284594_B</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>B</td>\n",
       "      <td>ZSCAN9_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002776</td>\n",
       "      <td>ZSCAN9</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19590052</th>\n",
       "      <td>ZZZ3</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>-1.147959</td>\n",
       "      <td>-1.203783</td>\n",
       "      <td>ENSG00000284594_B</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>B</td>\n",
       "      <td>ZZZ3_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001415</td>\n",
       "      <td>ZZZ3</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19590053 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               tf             gene      cms2      cms4          node_gene  \\\n",
       "0            ALX1  ENSG00000000003  1.639950  0.701865  ENSG00000000003_B   \n",
       "1            ALX3  ENSG00000000003  0.463252  0.087116  ENSG00000000003_B   \n",
       "2            ALX4  ENSG00000000003  1.347992  0.552226  ENSG00000000003_B   \n",
       "3              AR  ENSG00000000003 -0.986745 -0.862890  ENSG00000000003_B   \n",
       "4           ARGFX  ENSG00000000003  0.887296  0.290060  ENSG00000000003_B   \n",
       "...           ...              ...       ...       ...                ...   \n",
       "19590048   ZSCAN4  ENSG00000284594 -0.292792 -0.106729  ENSG00000284594_B   \n",
       "19590049   ZSCAN5  ENSG00000284594  0.836854  0.792892  ENSG00000284594_B   \n",
       "19590050  ZSCAN5C  ENSG00000284594 -0.752496 -0.782719  ENSG00000284594_B   \n",
       "19590051   ZSCAN9  ENSG00000284594 -1.703456 -1.760072  ENSG00000284594_B   \n",
       "19590052     ZZZ3  ENSG00000284594 -1.147959 -1.203783  ENSG00000284594_B   \n",
       "\n",
       "          module_gene  modularity_gene   node_name_gene node_type_gene  \\\n",
       "0                   7         0.002225  ENSG00000000003              B   \n",
       "1                   7         0.002225  ENSG00000000003              B   \n",
       "2                   7         0.002225  ENSG00000000003              B   \n",
       "3                   7         0.002225  ENSG00000000003              B   \n",
       "4                   7         0.002225  ENSG00000000003              B   \n",
       "...               ...              ...              ...            ...   \n",
       "19590048            2         0.000089  ENSG00000284594              B   \n",
       "19590049            2         0.000089  ENSG00000284594              B   \n",
       "19590050            2         0.000089  ENSG00000284594              B   \n",
       "19590051            2         0.000089  ENSG00000284594              B   \n",
       "19590052            2         0.000089  ENSG00000284594              B   \n",
       "\n",
       "            node_tf  module_tf  modularity_tf node_name_tf node_type_tf  \\\n",
       "0            ALX1_A          1       0.006097         ALX1            A   \n",
       "1            ALX3_A          2       0.011064         ALX3            A   \n",
       "2            ALX4_A          1       0.005248         ALX4            A   \n",
       "3              AR_A          2       0.001445           AR            A   \n",
       "4           ARGFX_A          2       0.012254        ARGFX            A   \n",
       "...             ...        ...            ...          ...          ...   \n",
       "19590048   ZSCAN4_A          2       0.002642       ZSCAN4            A   \n",
       "19590049   ZSCAN5_A          4       0.002277       ZSCAN5            A   \n",
       "19590050  ZSCAN5C_A          4       0.000994      ZSCAN5C            A   \n",
       "19590051   ZSCAN9_A          1       0.002776       ZSCAN9            A   \n",
       "19590052     ZZZ3_A          1       0.001415         ZZZ3            A   \n",
       "\n",
       "          same_module  edge_module  \n",
       "0               False            0  \n",
       "1               False            0  \n",
       "2               False            0  \n",
       "3               False            0  \n",
       "4               False            0  \n",
       "...               ...          ...  \n",
       "19590048         True            2  \n",
       "19590049        False            0  \n",
       "19590050        False            0  \n",
       "19590051        False            0  \n",
       "19590052        False            0  \n",
       "\n",
       "[19590053 rows x 16 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each edge, check if TF-gene are in the same module\n",
    "edge_membership['same_module'] = edge_membership.module_gene == edge_membership.module_tf\n",
    "edge_membership['edge_module'] = edge_membership['same_module'].astype(int) * edge_membership['module_gene'].astype(int)\n",
    "edge_membership"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
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        {
         "name": "tf",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "gene",
         "rawType": "object",
         "type": "string"
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       "shape": {
        "columns": 18,
        "rows": 19590053
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      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tf</th>\n",
       "      <th>gene</th>\n",
       "      <th>cms2</th>\n",
       "      <th>cms4</th>\n",
       "      <th>node_gene</th>\n",
       "      <th>module_gene</th>\n",
       "      <th>modularity_gene</th>\n",
       "      <th>node_name_gene</th>\n",
       "      <th>node_type_gene</th>\n",
       "      <th>node_tf</th>\n",
       "      <th>module_tf</th>\n",
       "      <th>modularity_tf</th>\n",
       "      <th>node_name_tf</th>\n",
       "      <th>node_type_tf</th>\n",
       "      <th>same_module</th>\n",
       "      <th>edge_module</th>\n",
       "      <th>diff_edge</th>\n",
       "      <th>diff_edge_abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALX1</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>1.639950</td>\n",
       "      <td>0.701865</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>ALX1_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.006097</td>\n",
       "      <td>ALX1</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.938085</td>\n",
       "      <td>0.938085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALX3</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>0.463252</td>\n",
       "      <td>0.087116</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>ALX3_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.011064</td>\n",
       "      <td>ALX3</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.376136</td>\n",
       "      <td>0.376136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALX4</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>1.347992</td>\n",
       "      <td>0.552226</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>ALX4_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005248</td>\n",
       "      <td>ALX4</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.795765</td>\n",
       "      <td>0.795765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>-0.986745</td>\n",
       "      <td>-0.862890</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>AR_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.001445</td>\n",
       "      <td>AR</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.123855</td>\n",
       "      <td>0.123855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARGFX</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>0.887296</td>\n",
       "      <td>0.290060</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>ARGFX_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.012254</td>\n",
       "      <td>ARGFX</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.597237</td>\n",
       "      <td>0.597237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19590048</th>\n",
       "      <td>ZSCAN4</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>-0.292792</td>\n",
       "      <td>-0.106729</td>\n",
       "      <td>ENSG00000284594_B</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>B</td>\n",
       "      <td>ZSCAN4_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.002642</td>\n",
       "      <td>ZSCAN4</td>\n",
       "      <td>A</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.186063</td>\n",
       "      <td>0.186063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19590049</th>\n",
       "      <td>ZSCAN5</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>0.836854</td>\n",
       "      <td>0.792892</td>\n",
       "      <td>ENSG00000284594_B</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>B</td>\n",
       "      <td>ZSCAN5_A</td>\n",
       "      <td>4</td>\n",
       "      <td>0.002277</td>\n",
       "      <td>ZSCAN5</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043963</td>\n",
       "      <td>0.043963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19590050</th>\n",
       "      <td>ZSCAN5C</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>-0.752496</td>\n",
       "      <td>-0.782719</td>\n",
       "      <td>ENSG00000284594_B</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>B</td>\n",
       "      <td>ZSCAN5C_A</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>ZSCAN5C</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.030223</td>\n",
       "      <td>0.030223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19590051</th>\n",
       "      <td>ZSCAN9</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>-1.703456</td>\n",
       "      <td>-1.760072</td>\n",
       "      <td>ENSG00000284594_B</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>B</td>\n",
       "      <td>ZSCAN9_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002776</td>\n",
       "      <td>ZSCAN9</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.056616</td>\n",
       "      <td>0.056616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19590052</th>\n",
       "      <td>ZZZ3</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>-1.147959</td>\n",
       "      <td>-1.203783</td>\n",
       "      <td>ENSG00000284594_B</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>B</td>\n",
       "      <td>ZZZ3_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001415</td>\n",
       "      <td>ZZZ3</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.055825</td>\n",
       "      <td>0.055825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19590053 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               tf             gene      cms2      cms4          node_gene  \\\n",
       "0            ALX1  ENSG00000000003  1.639950  0.701865  ENSG00000000003_B   \n",
       "1            ALX3  ENSG00000000003  0.463252  0.087116  ENSG00000000003_B   \n",
       "2            ALX4  ENSG00000000003  1.347992  0.552226  ENSG00000000003_B   \n",
       "3              AR  ENSG00000000003 -0.986745 -0.862890  ENSG00000000003_B   \n",
       "4           ARGFX  ENSG00000000003  0.887296  0.290060  ENSG00000000003_B   \n",
       "...           ...              ...       ...       ...                ...   \n",
       "19590048   ZSCAN4  ENSG00000284594 -0.292792 -0.106729  ENSG00000284594_B   \n",
       "19590049   ZSCAN5  ENSG00000284594  0.836854  0.792892  ENSG00000284594_B   \n",
       "19590050  ZSCAN5C  ENSG00000284594 -0.752496 -0.782719  ENSG00000284594_B   \n",
       "19590051   ZSCAN9  ENSG00000284594 -1.703456 -1.760072  ENSG00000284594_B   \n",
       "19590052     ZZZ3  ENSG00000284594 -1.147959 -1.203783  ENSG00000284594_B   \n",
       "\n",
       "          module_gene  modularity_gene   node_name_gene node_type_gene  \\\n",
       "0                   7         0.002225  ENSG00000000003              B   \n",
       "1                   7         0.002225  ENSG00000000003              B   \n",
       "2                   7         0.002225  ENSG00000000003              B   \n",
       "3                   7         0.002225  ENSG00000000003              B   \n",
       "4                   7         0.002225  ENSG00000000003              B   \n",
       "...               ...              ...              ...            ...   \n",
       "19590048            2         0.000089  ENSG00000284594              B   \n",
       "19590049            2         0.000089  ENSG00000284594              B   \n",
       "19590050            2         0.000089  ENSG00000284594              B   \n",
       "19590051            2         0.000089  ENSG00000284594              B   \n",
       "19590052            2         0.000089  ENSG00000284594              B   \n",
       "\n",
       "            node_tf  module_tf  modularity_tf node_name_tf node_type_tf  \\\n",
       "0            ALX1_A          1       0.006097         ALX1            A   \n",
       "1            ALX3_A          2       0.011064         ALX3            A   \n",
       "2            ALX4_A          1       0.005248         ALX4            A   \n",
       "3              AR_A          2       0.001445           AR            A   \n",
       "4           ARGFX_A          2       0.012254        ARGFX            A   \n",
       "...             ...        ...            ...          ...          ...   \n",
       "19590048   ZSCAN4_A          2       0.002642       ZSCAN4            A   \n",
       "19590049   ZSCAN5_A          4       0.002277       ZSCAN5            A   \n",
       "19590050  ZSCAN5C_A          4       0.000994      ZSCAN5C            A   \n",
       "19590051   ZSCAN9_A          1       0.002776       ZSCAN9            A   \n",
       "19590052     ZZZ3_A          1       0.001415         ZZZ3            A   \n",
       "\n",
       "          same_module  edge_module  diff_edge  diff_edge_abs  \n",
       "0               False            0   0.938085       0.938085  \n",
       "1               False            0   0.376136       0.376136  \n",
       "2               False            0   0.795765       0.795765  \n",
       "3               False            0  -0.123855       0.123855  \n",
       "4               False            0   0.597237       0.597237  \n",
       "...               ...          ...        ...            ...  \n",
       "19590048         True            2  -0.186063       0.186063  \n",
       "19590049        False            0   0.043963       0.043963  \n",
       "19590050        False            0   0.030223       0.030223  \n",
       "19590051        False            0   0.056616       0.056616  \n",
       "19590052        False            0   0.055825       0.055825  \n",
       "\n",
       "[19590053 rows x 18 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the edge difference and absolute edge difference\n",
    "edge_membership['diff_edge'] = edge_membership['cms2'] - edge_membership['cms4']\n",
    "edge_membership['diff_edge_abs'] = abs(edge_membership['diff_edge'])\n",
    "edge_membership"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
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        },
        {
         "name": "tf",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "gene",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "cms2",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "cms4",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "node_gene",
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         "type": "string"
        },
        {
         "name": "module_gene",
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        },
        {
         "name": "modularity_gene",
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         "type": "float"
        },
        {
         "name": "node_name_gene",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "node_type_gene",
         "rawType": "object",
         "type": "string"
        },
        {
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       "shape": {
        "columns": 19,
        "rows": 19590053
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      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tf</th>\n",
       "      <th>gene</th>\n",
       "      <th>cms2</th>\n",
       "      <th>cms4</th>\n",
       "      <th>node_gene</th>\n",
       "      <th>module_gene</th>\n",
       "      <th>modularity_gene</th>\n",
       "      <th>node_name_gene</th>\n",
       "      <th>node_type_gene</th>\n",
       "      <th>node_tf</th>\n",
       "      <th>module_tf</th>\n",
       "      <th>modularity_tf</th>\n",
       "      <th>node_name_tf</th>\n",
       "      <th>node_type_tf</th>\n",
       "      <th>same_module</th>\n",
       "      <th>edge_module</th>\n",
       "      <th>diff_edge</th>\n",
       "      <th>diff_edge_abs</th>\n",
       "      <th>is_edge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALX1</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>1.639950</td>\n",
       "      <td>0.701865</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>ALX1_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.006097</td>\n",
       "      <td>ALX1</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.938085</td>\n",
       "      <td>0.938085</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALX3</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>0.463252</td>\n",
       "      <td>0.087116</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>ALX3_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.011064</td>\n",
       "      <td>ALX3</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.376136</td>\n",
       "      <td>0.376136</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALX4</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>1.347992</td>\n",
       "      <td>0.552226</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>ALX4_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005248</td>\n",
       "      <td>ALX4</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.795765</td>\n",
       "      <td>0.795765</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>-0.986745</td>\n",
       "      <td>-0.862890</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>AR_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.001445</td>\n",
       "      <td>AR</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.123855</td>\n",
       "      <td>0.123855</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARGFX</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>0.887296</td>\n",
       "      <td>0.290060</td>\n",
       "      <td>ENSG00000000003_B</td>\n",
       "      <td>7</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>B</td>\n",
       "      <td>ARGFX_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.012254</td>\n",
       "      <td>ARGFX</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.597237</td>\n",
       "      <td>0.597237</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19590048</th>\n",
       "      <td>ZSCAN4</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>-0.292792</td>\n",
       "      <td>-0.106729</td>\n",
       "      <td>ENSG00000284594_B</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>B</td>\n",
       "      <td>ZSCAN4_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.002642</td>\n",
       "      <td>ZSCAN4</td>\n",
       "      <td>A</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.186063</td>\n",
       "      <td>0.186063</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19590049</th>\n",
       "      <td>ZSCAN5</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>0.836854</td>\n",
       "      <td>0.792892</td>\n",
       "      <td>ENSG00000284594_B</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>B</td>\n",
       "      <td>ZSCAN5_A</td>\n",
       "      <td>4</td>\n",
       "      <td>0.002277</td>\n",
       "      <td>ZSCAN5</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043963</td>\n",
       "      <td>0.043963</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19590050</th>\n",
       "      <td>ZSCAN5C</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>-0.752496</td>\n",
       "      <td>-0.782719</td>\n",
       "      <td>ENSG00000284594_B</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>B</td>\n",
       "      <td>ZSCAN5C_A</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>ZSCAN5C</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.030223</td>\n",
       "      <td>0.030223</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19590051</th>\n",
       "      <td>ZSCAN9</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>-1.703456</td>\n",
       "      <td>-1.760072</td>\n",
       "      <td>ENSG00000284594_B</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>B</td>\n",
       "      <td>ZSCAN9_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002776</td>\n",
       "      <td>ZSCAN9</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.056616</td>\n",
       "      <td>0.056616</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19590052</th>\n",
       "      <td>ZZZ3</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>-1.147959</td>\n",
       "      <td>-1.203783</td>\n",
       "      <td>ENSG00000284594_B</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>ENSG00000284594</td>\n",
       "      <td>B</td>\n",
       "      <td>ZZZ3_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001415</td>\n",
       "      <td>ZZZ3</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.055825</td>\n",
       "      <td>0.055825</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19590053 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               tf             gene      cms2      cms4          node_gene  \\\n",
       "0            ALX1  ENSG00000000003  1.639950  0.701865  ENSG00000000003_B   \n",
       "1            ALX3  ENSG00000000003  0.463252  0.087116  ENSG00000000003_B   \n",
       "2            ALX4  ENSG00000000003  1.347992  0.552226  ENSG00000000003_B   \n",
       "3              AR  ENSG00000000003 -0.986745 -0.862890  ENSG00000000003_B   \n",
       "4           ARGFX  ENSG00000000003  0.887296  0.290060  ENSG00000000003_B   \n",
       "...           ...              ...       ...       ...                ...   \n",
       "19590048   ZSCAN4  ENSG00000284594 -0.292792 -0.106729  ENSG00000284594_B   \n",
       "19590049   ZSCAN5  ENSG00000284594  0.836854  0.792892  ENSG00000284594_B   \n",
       "19590050  ZSCAN5C  ENSG00000284594 -0.752496 -0.782719  ENSG00000284594_B   \n",
       "19590051   ZSCAN9  ENSG00000284594 -1.703456 -1.760072  ENSG00000284594_B   \n",
       "19590052     ZZZ3  ENSG00000284594 -1.147959 -1.203783  ENSG00000284594_B   \n",
       "\n",
       "          module_gene  modularity_gene   node_name_gene node_type_gene  \\\n",
       "0                   7         0.002225  ENSG00000000003              B   \n",
       "1                   7         0.002225  ENSG00000000003              B   \n",
       "2                   7         0.002225  ENSG00000000003              B   \n",
       "3                   7         0.002225  ENSG00000000003              B   \n",
       "4                   7         0.002225  ENSG00000000003              B   \n",
       "...               ...              ...              ...            ...   \n",
       "19590048            2         0.000089  ENSG00000284594              B   \n",
       "19590049            2         0.000089  ENSG00000284594              B   \n",
       "19590050            2         0.000089  ENSG00000284594              B   \n",
       "19590051            2         0.000089  ENSG00000284594              B   \n",
       "19590052            2         0.000089  ENSG00000284594              B   \n",
       "\n",
       "            node_tf  module_tf  modularity_tf node_name_tf node_type_tf  \\\n",
       "0            ALX1_A          1       0.006097         ALX1            A   \n",
       "1            ALX3_A          2       0.011064         ALX3            A   \n",
       "2            ALX4_A          1       0.005248         ALX4            A   \n",
       "3              AR_A          2       0.001445           AR            A   \n",
       "4           ARGFX_A          2       0.012254        ARGFX            A   \n",
       "...             ...        ...            ...          ...          ...   \n",
       "19590048   ZSCAN4_A          2       0.002642       ZSCAN4            A   \n",
       "19590049   ZSCAN5_A          4       0.002277       ZSCAN5            A   \n",
       "19590050  ZSCAN5C_A          4       0.000994      ZSCAN5C            A   \n",
       "19590051   ZSCAN9_A          1       0.002776       ZSCAN9            A   \n",
       "19590052     ZZZ3_A          1       0.001415         ZZZ3            A   \n",
       "\n",
       "          same_module  edge_module  diff_edge  diff_edge_abs  is_edge  \n",
       "0               False            0   0.938085       0.938085        1  \n",
       "1               False            0   0.376136       0.376136        1  \n",
       "2               False            0   0.795765       0.795765        1  \n",
       "3               False            0  -0.123855       0.123855        1  \n",
       "4               False            0   0.597237       0.597237        1  \n",
       "...               ...          ...        ...            ...      ...  \n",
       "19590048         True            2  -0.186063       0.186063        1  \n",
       "19590049        False            0   0.043963       0.043963        1  \n",
       "19590050        False            0   0.030223       0.030223        1  \n",
       "19590051        False            0   0.056616       0.056616        1  \n",
       "19590052        False            0   0.055825       0.055825        1  \n",
       "\n",
       "[19590053 rows x 19 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We add an indicator to count the edges\n",
    "edge_membership['is_edge'] = 1\n",
    "edge_membership"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
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        {
         "name": "gene",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "module_gene",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "cms2",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "cms4",
         "rawType": "float64",
         "type": "float"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene</th>\n",
       "      <th>module_gene</th>\n",
       "      <th>cms2</th>\n",
       "      <th>cms4</th>\n",
       "      <th>diff_edge</th>\n",
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       "<p>39270 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "                  gene  module_gene     cms2     cms4  diff_edge  \\\n",
       "0      ENSG00000000003            7       15       19       -3.5   \n",
       "0      ENSG00000000003            7     -9.6      -99         90   \n",
       "1      ENSG00000000005            2 -1.8e+02 -1.9e+02        7.3   \n",
       "1      ENSG00000000005            2       20     -8.1         28   \n",
       "2      ENSG00000000419            2    2e+02    2e+02         -4   \n",
       "...                ...          ...      ...      ...        ...   \n",
       "19618  ENSG00000284564            1    1e+02    2e+02        -94   \n",
       "19619  ENSG00000284570            1  3.3e+02  3.8e+02        -50   \n",
       "19647  ENSG00000284570            1 -4.1e+02 -4.9e+02         76   \n",
       "19620  ENSG00000284594            2 -1.3e+02 -1.3e+02          5   \n",
       "19648  ENSG00000284594            2 -1.5e+02 -1.7e+02         14   \n",
       "\n",
       "       diff_edge_abs  is_edge  same_module  \n",
       "0                5.9       20         True  \n",
       "0            3.5e+02      977        False  \n",
       "1                 83      754        False  \n",
       "1                 29      243         True  \n",
       "2                 13      243         True  \n",
       "...              ...      ...          ...  \n",
       "19618             95      251         True  \n",
       "19619             51      251         True  \n",
       "19647        1.5e+02      746        False  \n",
       "19620             15      243         True  \n",
       "19648             44      754        False  \n",
       "\n",
       "[39270 rows x 8 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We compute the IN- and OUT-module degrees\n",
    "same_module_degrees = (edge_membership[edge_membership['same_module'] == True]).loc[:,['gene','module_gene','cms2','cms4','diff_edge','diff_edge_abs','is_edge']].groupby(['gene','module_gene']).sum().reset_index()\n",
    "same_module_degrees['same_module'] = True\n",
    "diff_module_degrees = (edge_membership[edge_membership['same_module'] == False]).loc[:,['gene','module_gene','cms2','cms4','diff_edge','diff_edge_abs','is_edge']].groupby(['gene','module_gene']).sum().reset_index()\n",
    "diff_module_degrees['same_module'] = False\n",
    "module_degrees = pd.concat([same_module_degrees, diff_module_degrees], axis=0)\n",
    "module_degrees.sort_values('gene')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FWER threshold: , 0.0016129032258064516\n",
      "Module 1,stat:385813.0, p: 0.0\n",
      "Module 2,stat:2143764.0, p: 1.8729015239018414e-32\n",
      "Module 3,stat:36981.0, p: 0.005900874893958021\n",
      "Module 4,stat:1433866.0, p: 0.0\n",
      "Module 5,stat:98890.0, p: 0.0002123507912633931\n",
      "Module 6,stat:104579.0, p: 2.2754201289414773e-19\n",
      "Module 7,stat:32643.0, p: 3.154133866856099e-05\n",
      "Module 8,stat:2663962.0, p: 8.132157319803211e-07\n",
      "Module 9,stat:218707.0, p: 7.614819402960453e-07\n",
      "Module 10,stat:955.0, p: 0.008536097523712965\n",
      "Module 11,stat:869.0, p: 1.5909601385626815e-09\n",
      "Module 12,stat:98998.0, p: 0.3369269658896168\n",
      "Module 13,stat:9620.0, p: 0.0619823983567691\n",
      "Module 14,stat:62.0, p: 0.1956329345703125\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def return_significance(p):\n",
    "    if p<1e-30:\n",
    "        return '****'\n",
    "    elif p<1e-3:\n",
    "        return '**'\n",
    "    elif p<1e-2:\n",
    "        return '*'\n",
    "    else:\n",
    "        return('')\n",
    "\n",
    "# seaborn set color to whitegrid\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "sigs = []\n",
    "n_bonferroni = len(module_degrees['module_gene'].unique())\n",
    "print(f'FWER threshold: , {0.05/n_bonferroni}')\n",
    "df_wilk_genes = pd.DataFrame()\n",
    "for module, tab in module_degrees[module_degrees['module_gene']<15].groupby('module_gene'):\n",
    "    stat, p = stats.wilcoxon(tab[tab['same_module']==True]['diff_edge'], tab[tab['same_module']==False]['diff_edge'])\n",
    "    a = return_significance(p*n_bonferroni)\n",
    "    sigs.append(a)\n",
    "    print(f'Module {module},stat:{stat}, p: {p}')\n",
    "    df_wilk_genes = pd.concat([df_wilk_genes, pd.DataFrame({'module': module, 'stat': stat, 'p': p, 'Bonferroni FWER': np.min([1., p*n_bonferroni]),\n",
    "                                                        \"mean IN-module degree\": np.mean(tab[tab['same_module']==True]['diff_edge']),\n",
    "                                                        \"mean OUT-module degree\": np.mean(tab[tab['same_module']==False]['diff_edge'])}, index=[0])], axis=0)\n",
    "\n",
    "sns.boxplot(x='module_gene', y='diff_edge', data=module_degrees[module_degrees['module_gene']<15], hue = 'same_module', palette = 'Set2')\n",
    "# annotate significance\n",
    "for i, txt in enumerate(sigs):\n",
    "    plt.annotate(txt, (i-0.2, np.max(module_degrees[module_degrees['module_gene']<15]['diff_edge'])), fontsize=14)\n",
    "plt.xlabel('Module')\n",
    "plt.ylabel('Sum per gene (cms2-cms4)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wilk_genes.module = df_wilk_genes.module.astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{rrrrrr}\n",
      "\\toprule\n",
      "module & stat & p & Bonferroni FWER & mean IN-module degree & mean OUT-module degree \\\\\n",
      "\\midrule\n",
      "1 & 385813.00 & 0.00E+00 & 0.00E+00 & -78.194178 & 43.655882 \\\\\n",
      "2 & 2143764.00 & 1.87E-32 & 5.81E-31 & 2.421301 & 10.373974 \\\\\n",
      "3 & 36981.00 & 5.90E-03 & 1.83E-01 & -0.132168 & 13.994770 \\\\\n",
      "4 & 1433866.00 & 0.00E+00 & 0.00E+00 & -63.115197 & 89.458204 \\\\\n",
      "5 & 98890.00 & 2.12E-04 & 6.58E-03 & 0.165063 & 12.623169 \\\\\n",
      "6 & 104579.00 & 2.28E-19 & 7.05E-18 & 0.347839 & 28.194577 \\\\\n",
      "7 & 32643.00 & 3.15E-05 & 9.78E-04 & -0.414056 & 21.782637 \\\\\n",
      "8 & 2663962.00 & 8.13E-07 & 2.52E-05 & -4.844230 & 2.151773 \\\\\n",
      "9 & 218707.00 & 7.61E-07 & 2.36E-05 & -1.094298 & 14.393717 \\\\\n",
      "10 & 955.00 & 8.54E-03 & 2.65E-01 & 0.074718 & 39.860753 \\\\\n",
      "11 & 869.00 & 1.59E-09 & 4.93E-08 & 0.659262 & 53.029421 \\\\\n",
      "12 & 98998.00 & 3.37E-01 & 1.00E+00 & -0.456192 & 9.702970 \\\\\n",
      "13 & 9620.00 & 6.20E-02 & 1.00E+00 & -0.154742 & 14.593134 \\\\\n",
      "14 & 62.00 & 1.96E-01 & 1.00E+00 & 0.024142 & -20.562472 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pd.options.display.float_format = None\n",
    "pd.set_option('display.float_format', '{:.2g}'.format)\n",
    "print(df_wilk_genes.to_latex(index=False,\n",
    "                  formatters={\"name\": str.upper,\"stat\": \"{:.2f}\".format, \"p\": \"{:.2E}\".format, \"Bonferroni FWER\": \"{:.2E}\".format},\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we do the same for TFs (compute degrees and differential degrees)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "same_module_degrees = (edge_membership[edge_membership['same_module'] == True]).loc[:,['tf','module_tf','cms2','cms4','diff_edge','diff_edge_abs','is_edge']].groupby(['tf','module_tf']).sum().reset_index()\n",
    "same_module_degrees['same_module'] = True\n",
    "diff_module_degrees = (edge_membership[edge_membership['same_module'] == False]).loc[:,['tf','module_tf','cms2','cms4','diff_edge','diff_edge_abs','is_edge']].groupby(['tf','module_tf']).sum().reset_index()\n",
    "diff_module_degrees['same_module'] = False\n",
    "tf_degrees = pd.concat([same_module_degrees, diff_module_degrees], axis=0)\n",
    "tf_degrees.sort_values('tf')\n",
    "\n",
    "tf_degrees['differential_degree'] = tf_degrees['cms2'] - tf_degrees['cms4']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "tf",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "module_tf",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "cms2",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "cms4",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "diff_edge",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "diff_edge_abs",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "is_edge",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "same_module",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "differential_degree",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "conversionMethod": "pd.DataFrame",
       "ref": "f7609b77-c035-4663-9419-1f8bb5833ed3",
       "rows": [
        [
         "0",
         "ALX1",
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        ],
        [
         "1",
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         "-45.23020673078099"
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        [
         "2",
         "ALX4",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tf</th>\n",
       "      <th>module_tf</th>\n",
       "      <th>cms2</th>\n",
       "      <th>cms4</th>\n",
       "      <th>diff_edge</th>\n",
       "      <th>diff_edge_abs</th>\n",
       "      <th>is_edge</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALX1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.6e+03</td>\n",
       "      <td>7.5e+03</td>\n",
       "      <td>-1.8e+03</td>\n",
       "      <td>2e+03</td>\n",
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       "      <td>True</td>\n",
       "      <td>-1.8e+03</td>\n",
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       "      <th>1</th>\n",
       "      <td>ALX3</td>\n",
       "      <td>2</td>\n",
       "      <td>3.9e+03</td>\n",
       "      <td>3.9e+03</td>\n",
       "      <td>-45</td>\n",
       "      <td>5.4e+02</td>\n",
       "      <td>3351</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALX4</td>\n",
       "      <td>1</td>\n",
       "      <td>4.7e+03</td>\n",
       "      <td>6.4e+03</td>\n",
       "      <td>-1.6e+03</td>\n",
       "      <td>1.8e+03</td>\n",
       "      <td>3601</td>\n",
       "      <td>True</td>\n",
       "      <td>-1.6e+03</td>\n",
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       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.5e+03</td>\n",
       "      <td>-1.6e+03</td>\n",
       "      <td>82</td>\n",
       "      <td>3.3e+02</td>\n",
       "      <td>3351</td>\n",
       "      <td>True</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARGFX</td>\n",
       "      <td>2</td>\n",
       "      <td>6.1e+03</td>\n",
       "      <td>6.2e+03</td>\n",
       "      <td>-89</td>\n",
       "      <td>8.7e+02</td>\n",
       "      <td>3351</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>ZSCAN4</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.2e+03</td>\n",
       "      <td>-2.4e+03</td>\n",
       "      <td>2e+02</td>\n",
       "      <td>4.7e+03</td>\n",
       "      <td>16298</td>\n",
       "      <td>False</td>\n",
       "      <td>2e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>ZSCAN5</td>\n",
       "      <td>4</td>\n",
       "      <td>-3.5e+03</td>\n",
       "      <td>-4.7e+03</td>\n",
       "      <td>1.2e+03</td>\n",
       "      <td>3.1e+03</td>\n",
       "      <td>14829</td>\n",
       "      <td>False</td>\n",
       "      <td>1.2e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>ZSCAN5C</td>\n",
       "      <td>4</td>\n",
       "      <td>-81</td>\n",
       "      <td>1.8e+02</td>\n",
       "      <td>-2.6e+02</td>\n",
       "      <td>2.4e+03</td>\n",
       "      <td>14829</td>\n",
       "      <td>False</td>\n",
       "      <td>-2.6e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>ZSCAN9</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9e+02</td>\n",
       "      <td>-1.2e+03</td>\n",
       "      <td>1.4e+03</td>\n",
       "      <td>5.4e+03</td>\n",
       "      <td>16048</td>\n",
       "      <td>False</td>\n",
       "      <td>1.4e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>ZZZ3</td>\n",
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       "      <td>-2.3e+03</td>\n",
       "      <td>-3.1e+03</td>\n",
       "      <td>8.3e+02</td>\n",
       "      <td>3.2e+03</td>\n",
       "      <td>16048</td>\n",
       "      <td>False</td>\n",
       "      <td>8.3e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>1994 rows × 9 columns</p>\n",
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      "text/plain": [
       "          tf  module_tf     cms2     cms4  diff_edge  diff_edge_abs  is_edge  \\\n",
       "0       ALX1          1  5.6e+03  7.5e+03   -1.8e+03          2e+03     3601   \n",
       "1       ALX3          2  3.9e+03  3.9e+03        -45        5.4e+02     3351   \n",
       "2       ALX4          1  4.7e+03  6.4e+03   -1.6e+03        1.8e+03     3601   \n",
       "3         AR          2 -1.5e+03 -1.6e+03         82        3.3e+02     3351   \n",
       "4      ARGFX          2  6.1e+03  6.2e+03        -89        8.7e+02     3351   \n",
       "..       ...        ...      ...      ...        ...            ...      ...   \n",
       "992   ZSCAN4          2 -2.2e+03 -2.4e+03      2e+02        4.7e+03    16298   \n",
       "993   ZSCAN5          4 -3.5e+03 -4.7e+03    1.2e+03        3.1e+03    14829   \n",
       "994  ZSCAN5C          4      -81  1.8e+02   -2.6e+02        2.4e+03    14829   \n",
       "995   ZSCAN9          1  1.9e+02 -1.2e+03    1.4e+03        5.4e+03    16048   \n",
       "996     ZZZ3          1 -2.3e+03 -3.1e+03    8.3e+02        3.2e+03    16048   \n",
       "\n",
       "     same_module  differential_degree  \n",
       "0           True             -1.8e+03  \n",
       "1           True                  -45  \n",
       "2           True             -1.6e+03  \n",
       "3           True                   82  \n",
       "4           True                  -89  \n",
       "..           ...                  ...  \n",
       "992        False                2e+02  \n",
       "993        False              1.2e+03  \n",
       "994        False             -2.6e+02  \n",
       "995        False              1.4e+03  \n",
       "996        False              8.3e+02  \n",
       "\n",
       "[1994 rows x 9 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_degrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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        {
         "name": "index",
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        {
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         "type": "float"
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         "type": "float"
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        {
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         "type": "float"
        },
        {
         "name": "diff_edge_abs",
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         "type": "float"
        },
        {
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         "type": "integer"
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        {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>tf</th>\n",
       "      <th>module_tf</th>\n",
       "      <th>cms2</th>\n",
       "      <th>cms4</th>\n",
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      ],
      "text/plain": [
       "          tf  module_tf  cms2  cms4  diff_edge  diff_edge_abs  is_edge  \\\n",
       "817  ZNF385D         14    87    87       0.46            2.3       19   \n",
       "\n",
       "     same_module  differential_degree  \n",
       "817         True                 0.46  "
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     "execution_count": 58,
     "metadata": {},
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    }
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   "source": [
    "tab[tab['same_module']==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FWER threshold: , 0.0035714285714285718\n",
      "Module 1,stat:40.0, p: 1.0248886991655897e-42\n",
      "Module 2,stat:7088.0, p: 1.7654741798452378e-12\n",
      "Module 3,stat:11.0, p: 0.0008392333984375\n",
      "Module 4,stat:788.0, p: 9.046025939011586e-42\n",
      "Module 5,stat:207.0, p: 0.83135736733675\n",
      "Module 6,stat:2.0, p: 1.3969838619232178e-09\n",
      "Module 7,stat:83.0, p: 0.4304332733154297\n",
      "Module 8,stat:254.0, p: 1.909507566350996e-06\n",
      "Module 9,stat:136.0, p: 0.00011535796875250526\n",
      "Module 10,stat:3.0, p: 1.0\n",
      "Module 11,stat:0.0, p: 0.03125\n",
      "Module 12,stat:28.0, p: 0.0006937980651855469\n",
      "Module 13,stat:0.0, p: 0.015625\n",
      "Module 14,stat:0.0, p: 1.0\n"
     ]
    },
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigs = []\n",
    "n_bonferroni = len(tf_degrees['module_tf'].unique())\n",
    "print(f'FWER threshold: , {0.05/n_bonferroni}')\n",
    "df_wilk_tfs = pd.DataFrame()\n",
    "\n",
    "for module, tab in tf_degrees[tf_degrees['module_tf']<15].groupby('module_tf'):\n",
    "    \n",
    "\n",
    "    stat, p = stats.wilcoxon(tab[tab['same_module']==True]['differential_degree'], tab[tab['same_module']==False]['differential_degree'])\n",
    "    a = return_significance(p*n_bonferroni)\n",
    "    sigs.append(a)\n",
    "    print(f'Module {module},stat:{stat}, p: {p}')\n",
    "    \n",
    "    df_wilk_tfs = pd.concat([df_wilk_tfs, pd.DataFrame({'module': module, \n",
    "                                                        'stat': stat, \n",
    "                                                        'p': p, \n",
    "                                                        'Bonferroni FWER': np.min([1., p*n_bonferroni]),\n",
    "                                                        \"mean IN-module degree\": np.mean(tab[tab['same_module']==True]['differential_degree']),\n",
    "                                                        \"mean OUT-module degree\": np.mean(tab[tab['same_module']==False]['differential_degree'])                                                       \n",
    "                                                        }, index=[0])], axis=0)\n",
    "\n",
    "\n",
    "sns.boxplot(x='module_tf', y='differential_degree', data=tf_degrees[tf_degrees['module_tf']<15], hue = 'same_module', palette = 'Set1')\n",
    "# annotate significance\n",
    "for i, txt in enumerate(sigs):\n",
    "    plt.annotate(txt, (i-0.2, np.max(tf_degrees[tf_degrees['module_tf']<15]['differential_degree'])), fontsize=14)\n",
    "plt.xlabel('Module')\n",
    "plt.ylabel('Differential degree per TF')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{rrrrrr}\n",
      "\\toprule\n",
      "module & stat & p & Bonferroni FWER & mean IN-module degree & mean OUT-module degree \\\\\n",
      "\\midrule\n",
      "1 & 40.00 & 1.02E-42 & 1.43E-41 & -1121.821654 & 1331.685448 \\\\\n",
      "2 & 7088.00 & 1.77E-12 & 2.47E-11 & 33.390045 & 133.276001 \\\\\n",
      "3 & 11.00 & 8.39E-04 & 1.17E-02 & -3.249790 & -110.794156 \\\\\n",
      "4 & 788.00 & 9.05E-42 & 1.27E-40 & -1139.382961 & 1268.302363 \\\\\n",
      "5 & 207.00 & 8.31E-01 & 1.00E+00 & 3.910279 & -2.354879 \\\\\n",
      "6 & 2.00 & 1.40E-09 & 1.96E-08 & 8.815545 & 115.128064 \\\\\n",
      "7 & 83.00 & 4.30E-01 & 1.00E+00 & -8.550258 & 13.984968 \\\\\n",
      "8 & 254.00 & 1.91E-06 & 2.67E-05 & -282.032701 & 170.765688 \\\\\n",
      "9 & 136.00 & 1.15E-04 & 1.62E-03 & -28.205519 & -121.859152 \\\\\n",
      "10 & 3.00 & 1.00E+00 & 1.00E+00 & 1.892866 & -2.441624 \\\\\n",
      "11 & 0.00 & 3.12E-02 & 4.38E-01 & 11.427210 & 139.448435 \\\\\n",
      "12 & 28.00 & 6.94E-04 & 9.71E-03 & -13.333253 & -158.706423 \\\\\n",
      "13 & 0.00 & 1.56E-02 & 2.19E-01 & -4.686480 & 206.724260 \\\\\n",
      "14 & 0.00 & 1.00E+00 & 1.00E+00 & 0.458702 & -102.928285 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pd.options.display.float_format = None\n",
    "pd.set_option('display.float_format', '{:.2g}'.format)\n",
    "print(df_wilk_tfs.to_latex(index=False,\n",
    "                  formatters={\"name\": str.upper,\"stat\": \"{:.2f}\".format, \"p\": \"{:.2E}\".format, \"Bonferroni FWER\": \"{:.2E}\".format},\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the leading TF by modularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node</th>\n",
       "      <th>module</th>\n",
       "      <th>modularity</th>\n",
       "      <th>node_name</th>\n",
       "      <th>node_type</th>\n",
       "      <th>n_tf</th>\n",
       "      <th>raw_modularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALX1_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>ALX1</td>\n",
       "      <td>A</td>\n",
       "      <td>251</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALX3_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.011</td>\n",
       "      <td>ALX3</td>\n",
       "      <td>A</td>\n",
       "      <td>243</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALX4_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>ALX4</td>\n",
       "      <td>A</td>\n",
       "      <td>251</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>AR</td>\n",
       "      <td>A</td>\n",
       "      <td>243</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARGFX_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.012</td>\n",
       "      <td>ARGFX</td>\n",
       "      <td>A</td>\n",
       "      <td>243</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>ZSCAN4_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>ZSCAN4</td>\n",
       "      <td>A</td>\n",
       "      <td>243</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>ZSCAN5_A</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>ZSCAN5</td>\n",
       "      <td>A</td>\n",
       "      <td>267</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>ZSCAN5C_A</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00099</td>\n",
       "      <td>ZSCAN5C</td>\n",
       "      <td>A</td>\n",
       "      <td>267</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>ZSCAN9_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>ZSCAN9</td>\n",
       "      <td>A</td>\n",
       "      <td>251</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>ZZZ3_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>ZZZ3</td>\n",
       "      <td>A</td>\n",
       "      <td>251</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>997 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          node  module  modularity node_name node_type  n_tf  raw_modularity\n",
       "0       ALX1_A       1      0.0061      ALX1         A   251             1.5\n",
       "1       ALX3_A       2       0.011      ALX3         A   243             2.7\n",
       "2       ALX4_A       1      0.0052      ALX4         A   251             1.3\n",
       "3         AR_A       2      0.0014        AR         A   243            0.35\n",
       "4      ARGFX_A       2       0.012     ARGFX         A   243               3\n",
       "..         ...     ...         ...       ...       ...   ...             ...\n",
       "992   ZSCAN4_A       2      0.0026    ZSCAN4         A   243            0.64\n",
       "993   ZSCAN5_A       4      0.0023    ZSCAN5         A   267            0.61\n",
       "994  ZSCAN5C_A       4     0.00099   ZSCAN5C         A   267            0.27\n",
       "995   ZSCAN9_A       1      0.0028    ZSCAN9         A   251             0.7\n",
       "996     ZZZ3_A       1      0.0014      ZZZ3         A   251            0.36\n",
       "\n",
       "[997 rows x 7 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "membership_tf = membership[membership['node_type']=='A']\n",
    "map_n_tf = membership_tf.loc[:,['module','node_name']].groupby('module').count().rename(columns={'node_name':'n_tf'})\n",
    "membership_tf = membership_tf.merge(map_n_tf, left_on='module', right_index=True)\n",
    "membership_tf['raw_modularity'] = membership_tf['modularity'] * membership_tf['n_tf']\n",
    "membership_tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "membership_gene = membership[membership['node_type']=='B']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
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        ],
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         "MIXL1",
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         "2.615735494875281"
        ],
        [
         "725",
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         "4",
         "0.0097513907539512",
         "ZNF132",
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         "2.6036213313049705"
        ],
        [
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         "MAZ",
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        ],
        [
         "831",
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         "4",
         "0.0096916670572144",
         "ZNF432",
         "A",
         "267",
         "2.5876751042762445"
        ],
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         "256",
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         "2",
         "0.0106328788855571",
         "HOXB3",
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        ],
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         "LHX3",
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         "2.521505673970164"
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        [
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         "4",
         "0.0093444753658165",
         "ZNF548",
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         "267",
         "2.4949749226730056"
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        "rows": 852
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      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node</th>\n",
       "      <th>module</th>\n",
       "      <th>modularity</th>\n",
       "      <th>node_name</th>\n",
       "      <th>node_type</th>\n",
       "      <th>n_tf</th>\n",
       "      <th>raw_modularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>ZNF180_A</td>\n",
       "      <td>4</td>\n",
       "      <td>0.014</td>\n",
       "      <td>ZNF180</td>\n",
       "      <td>A</td>\n",
       "      <td>267</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>ZNF529_A</td>\n",
       "      <td>4</td>\n",
       "      <td>0.014</td>\n",
       "      <td>ZNF529</td>\n",
       "      <td>A</td>\n",
       "      <td>267</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>LHX9_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.015</td>\n",
       "      <td>LHX9</td>\n",
       "      <td>A</td>\n",
       "      <td>243</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>LHX8_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.015</td>\n",
       "      <td>LHX8</td>\n",
       "      <td>A</td>\n",
       "      <td>243</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>LBX1_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.015</td>\n",
       "      <td>LBX1</td>\n",
       "      <td>A</td>\n",
       "      <td>243</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>ZBTB3_A</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>ZBTB3</td>\n",
       "      <td>A</td>\n",
       "      <td>59</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>E2F2_A</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>E2F2</td>\n",
       "      <td>A</td>\n",
       "      <td>32</td>\n",
       "      <td>0.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>ZNF449_A</td>\n",
       "      <td>8</td>\n",
       "      <td>0.00094</td>\n",
       "      <td>ZNF449</td>\n",
       "      <td>A</td>\n",
       "      <td>59</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>NFE2_A</td>\n",
       "      <td>8</td>\n",
       "      <td>0.00059</td>\n",
       "      <td>NFE2</td>\n",
       "      <td>A</td>\n",
       "      <td>59</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>GMEB1_A</td>\n",
       "      <td>2</td>\n",
       "      <td>1.6e-05</td>\n",
       "      <td>GMEB1</td>\n",
       "      <td>A</td>\n",
       "      <td>243</td>\n",
       "      <td>0.0039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>852 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         node  module  modularity node_name node_type  n_tf  raw_modularity\n",
       "742  ZNF180_A       4       0.014    ZNF180         A   267             3.7\n",
       "867  ZNF529_A       4       0.014    ZNF529         A   267             3.7\n",
       "336    LHX9_A       2       0.015      LHX9         A   243             3.6\n",
       "335    LHX8_A       2       0.015      LHX8         A   243             3.5\n",
       "325    LBX1_A       2       0.015      LBX1         A   243             3.5\n",
       "..        ...     ...         ...       ...       ...   ...             ...\n",
       "675   ZBTB3_A       8      0.0011     ZBTB3         A    59           0.062\n",
       "89     E2F2_A       6      0.0018      E2F2         A    32           0.056\n",
       "842  ZNF449_A       8     0.00094    ZNF449         A    59           0.055\n",
       "398    NFE2_A       8     0.00059      NFE2         A    59           0.035\n",
       "205   GMEB1_A       2     1.6e-05     GMEB1         A   243          0.0039\n",
       "\n",
       "[852 rows x 7 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cluster of interest 1,2 4,6,8\n",
    "membership_tf[membership_tf['module'].isin([1,2,4,6,8])].sort_values(['raw_modularity'], ascending=[False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "['ZNF334', 'FOXD2', 'HMG20B', 'ZNF33B', 'FOXD3', 'POU3F3', 'VSX2', 'ARID5B', 'NR1H4', 'ZNF25']\n",
      "2\n",
      "['LHX9', 'LHX8', 'LBX1', 'PBX4', 'DLX6', 'LHX2', 'SHOX2', 'PRRX1', 'PRRX2', 'SHOX']\n",
      "3\n",
      "['ARNT', 'ARNT2', 'BHLHE41', 'ARNTL', 'TFE3', 'BHLHE40', 'MLXIPL', 'MLX', 'SREBF2', 'USF1']\n",
      "4\n",
      "['ZNF180', 'ZNF529', 'ZNF341', 'ZNF444', 'ZNF467', 'PRDM9', 'ZNF468', 'ZNF304', 'ZNF383', 'ZNF880']\n",
      "5\n",
      "['FOSB', 'FOSL2', 'CREB5', 'ATF3', 'CREM', 'ATF2', 'CREB3L4', 'CREB1', 'FOSL1', 'ATF7']\n",
      "6\n",
      "['OLIG3', 'OLIG1', 'ATOH7', 'BHLHE23', 'NEUROD2', 'NEUROG1', 'NEUROG2', 'BHLHE22', 'PPARD', 'RXRA']\n",
      "7\n",
      "['HEY1', 'HEY2', 'HEYL', 'HES2', 'HES5', 'HES1', 'HES7', 'MYCN', 'HOXD1', 'HOXC8']\n",
      "8\n",
      "['ZNF225', 'ZNF487', 'ZNF235', 'ZNF287', 'ZNF443', 'PRDM6', 'ZNF182', 'ZNF496', 'ZNF181', 'ZNF98']\n",
      "9\n",
      "['ELK4', 'ELK3', 'ETV4', 'ETV1', 'ETV3', 'ERF', 'GABPA', 'FLI1', 'FEV', 'ELK1']\n",
      "10\n",
      "['GLI3', 'GLI1', 'GLI2']\n",
      "11\n",
      "['HOXC9', 'HOXB9', 'HOXA11', 'HOXA10', 'HOXD11', 'KDM2B']\n",
      "12\n",
      "['KLF6', 'KLF12', 'KLF14', 'KLF2', 'SP3', 'KLF4', 'KLF3', 'SP9', 'KLF5', 'SP8']\n",
      "13\n",
      "['TGIF2', 'PKNOX1', 'TGIF1', 'PKNOX2', 'TGIF2LX', 'TGIF2LY', 'MEIS2']\n",
      "14\n",
      "['ZNF385D']\n"
     ]
    }
   ],
   "source": [
    "top_tfs = pd.DataFrame()\n",
    "for k, tab in membership_tf.groupby('module'):\n",
    "    print(k)\n",
    "    # select the first 100 edges based on the first 10 tf modularities and the first 10 gene modularities\n",
    "    print(tab.sort_values('modularity', ascending=False)['node_name'].values[:10].tolist())\n",
    "    top_tfs = pd.concat([top_tfs, tab.sort_values('modularity', ascending=False).head(10)], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "node",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "module",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "modularity",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "node_name",
         "rawType": "object",
         "type": "string"
        },
        {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node</th>\n",
       "      <th>module</th>\n",
       "      <th>modularity</th>\n",
       "      <th>node_name</th>\n",
       "      <th>node_type</th>\n",
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       "      <th>raw_modularity</th>\n",
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       "      <th>802</th>\n",
       "      <td>ZNF334_A</td>\n",
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       "      <td>0.011</td>\n",
       "      <td>ZNF334</td>\n",
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       "      <th>233</th>\n",
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       "      <th>155</th>\n",
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       "      <td>FOXD3</td>\n",
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       "      <th>488</th>\n",
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       "      <th>659</th>\n",
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       "      <td>2.3</td>\n",
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       "      <th>6</th>\n",
       "      <td>ARID5B_A</td>\n",
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       "      <td>0.0088</td>\n",
       "      <td>ARID5B</td>\n",
       "      <td>A</td>\n",
       "      <td>251</td>\n",
       "      <td>2.2</td>\n",
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       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>NR1H4_A</td>\n",
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       "      <td>0.0087</td>\n",
       "      <td>NR1H4</td>\n",
       "      <td>A</td>\n",
       "      <td>251</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>ZNF25_A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>ZNF25</td>\n",
       "      <td>A</td>\n",
       "      <td>251</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>LHX9_A</td>\n",
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       "      <td>0.015</td>\n",
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       "      <td>3.6</td>\n",
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       "      <th>335</th>\n",
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       "      <td>0.015</td>\n",
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       "      <td>3.5</td>\n",
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       "      <th>325</th>\n",
       "      <td>LBX1_A</td>\n",
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       "      <td>0.015</td>\n",
       "      <td>LBX1</td>\n",
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       "      <td>243</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>PBX4_A</td>\n",
       "      <td>2</td>\n",
       "      <td>0.014</td>\n",
       "      <td>PBX4</td>\n",
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       "      <td>243</td>\n",
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       "      <th>73</th>\n",
       "      <td>DLX6_A</td>\n",
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       "      <th>330</th>\n",
       "      <td>LHX2_A</td>\n",
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       "      <td>0.014</td>\n",
       "      <td>LHX2</td>\n",
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       "      <td>243</td>\n",
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       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>SHOX2_A</td>\n",
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       "      <td>0.014</td>\n",
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       "      <th>503</th>\n",
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       "      <th>504</th>\n",
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       "      <th>537</th>\n",
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       "      <td>0.014</td>\n",
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       "      <td>3.4</td>\n",
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       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>ZNF180_A</td>\n",
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       "      <td>ZNF180</td>\n",
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       "      <td>267</td>\n",
       "      <td>3.7</td>\n",
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       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>ZNF529_A</td>\n",
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       "      <td>ZNF529</td>\n",
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       "      <th>806</th>\n",
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       "      <th>840</th>\n",
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       "      <th>846</th>\n",
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       "      <td>3.2</td>\n",
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       "      <th>499</th>\n",
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       "      <th>847</th>\n",
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       "      <td>3.1</td>\n",
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       "      <th>791</th>\n",
       "      <td>ZNF304_A</td>\n",
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       "      <td>0.012</td>\n",
       "      <td>ZNF304</td>\n",
       "      <td>A</td>\n",
       "      <td>267</td>\n",
       "      <td>3.1</td>\n",
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       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>ZNF383_A</td>\n",
       "      <td>4</td>\n",
       "      <td>0.011</td>\n",
       "      <td>ZNF383</td>\n",
       "      <td>A</td>\n",
       "      <td>267</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>ZNF880_A</td>\n",
       "      <td>4</td>\n",
       "      <td>0.011</td>\n",
       "      <td>ZNF880</td>\n",
       "      <td>A</td>\n",
       "      <td>267</td>\n",
       "      <td>2.8</td>\n",
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      ],
      "text/plain": [
       "         node  module  modularity node_name node_type  n_tf  raw_modularity\n",
       "802  ZNF334_A       1       0.011    ZNF334         A   251             2.7\n",
       "154   FOXD2_A       1      0.0097     FOXD2         A   251             2.4\n",
       "233  HMG20B_A       1      0.0095    HMG20B         A   251             2.4\n",
       "804  ZNF33B_A       1      0.0091    ZNF33B         A   251             2.3\n",
       "155   FOXD3_A       1      0.0091     FOXD3         A   251             2.3\n",
       "488  POU3F3_A       1      0.0091    POU3F3         A   251             2.3\n",
       "659    VSX2_A       1       0.009      VSX2         A   251             2.3\n",
       "6    ARID5B_A       1      0.0088    ARID5B         A   251             2.2\n",
       "429   NR1H4_A       1      0.0087     NR1H4         A   251             2.2\n",
       "766   ZNF25_A       1      0.0086     ZNF25         A   251             2.1\n",
       "336    LHX9_A       2       0.015      LHX9         A   243             3.6\n",
       "335    LHX8_A       2       0.015      LHX8         A   243             3.5\n",
       "325    LBX1_A       2       0.015      LBX1         A   243             3.5\n",
       "470    PBX4_A       2       0.014      PBX4         A   243             3.5\n",
       "73     DLX6_A       2       0.014      DLX6         A   243             3.5\n",
       "330    LHX2_A       2       0.014      LHX2         A   243             3.5\n",
       "538   SHOX2_A       2       0.014     SHOX2         A   243             3.5\n",
       "503   PRRX1_A       2       0.014     PRRX1         A   243             3.4\n",
       "504   PRRX2_A       2       0.014     PRRX2         A   243             3.4\n",
       "537    SHOX_A       2       0.014      SHOX         A   243             3.4\n",
       "742  ZNF180_A       4       0.014    ZNF180         A   267             3.7\n",
       "867  ZNF529_A       4       0.014    ZNF529         A   267             3.7\n",
       "806  ZNF341_A       4       0.013    ZNF341         A   267             3.4\n",
       "840  ZNF444_A       4       0.012    ZNF444         A   267             3.3\n",
       "846  ZNF467_A       4       0.012    ZNF467         A   267             3.2\n",
       "499   PRDM9_A       4       0.012     PRDM9         A   267             3.2\n",
       "847  ZNF468_A       4       0.012    ZNF468         A   267             3.1\n",
       "791  ZNF304_A       4       0.012    ZNF304         A   267             3.1\n",
       "815  ZNF383_A       4       0.011    ZNF383         A   267               3\n",
       "979  ZNF880_A       4       0.011    ZNF880         A   267             2.8"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_tfs[top_tfs['module'].isin([1,2,4])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/violafanfani/miniconda3/envs/m1-ml-py10/lib/python3.12/site-packages/seaborn/categorical.py:2761: UserWarning: catplot is a figure-level function and does not accept target axes. You may wish to try barplot\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x2500 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"notebook\", font_scale=1.5)\n",
    "g1 = sns.catplot(col='module', x='modularity', data=top_tfs, y='node_name', palette = 'Set2', hue = 'module', ax = ax, col_wrap=3, height=5, sharey=False, kind = 'bar', legend = False, sharex=False)\n",
    "g1.set_xlabels('Modularity')\n",
    "g1.set_ylabels('')\n",
    "g1.fig.savefig(outputDir + 'top_tfs_modules_all.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/violafanfani/miniconda3/envs/m1-ml-py10/lib/python3.12/site-packages/seaborn/categorical.py:2761: UserWarning: catplot is a figure-level function and does not accept target axes. You may wish to try barplot\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"notebook\")\n",
    "g1 = sns.catplot(col='module', x='modularity', data=top_tfs[top_tfs['module'].isin([1,2,4])], y='node_name', palette = 'Set2', hue = 'module', ax = ax, col_wrap=3, height=5, sharey=False, kind = 'bar', legend = False)\n",
    "g1.set_xlabels('Modularity')\n",
    "g1.set_ylabels('')\n",
    "g1.fig.savefig(outputDir + 'top_tfs_modules_1_2_4.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization of the pathway analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "m1-ml-py10",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
